Data Science -AI/ML

Zorba AI; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Zorba AI in Chennai, India, is seeking a qualified candidate to develop and deploy cutting-edge GenAI applications. You will design machine learning models for anomaly detection and advanced Natural Language Processing (NLP) pipelines, requiring proficiency in Python and experience with relevant AI/ML libraries. This role involves collaboration with backend teams, focusing on responsible AI practices and model performance monitoring. Join us to shape the future of AI technology in a dynamic and innovative environment.

Responsibilities

• Design and implement machine learning models for anomaly detection.
• Develop and maintain NLP pipelines for document processing.
• Preprocess and clean structured and unstructured data.
• Implement vectorization techniques and integrate with databases.
• Work with embedding models to support semantic search.
• Fine-tune LLMs for summarization and classification.
• Collaborate with backend engineers to expose ML models via APIs.
• Monitor model performance using precision, recall, and ROC-AUC.

Skills

Experience in developing and deploying GenAI/LLM powered applications/products

Proficient in Python

Experience with NLP techniques

Hands-on experience with anomaly detection techniques

Familiarity with vectorization techniques

Exposure to LLMs and prompt engineering

Tools

TensorFlow

PyTorch

Hugging Face Transformers

LangChain

FAISS

Job description

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Experience in developing and deploying GenAI/LLM powered applications/products
• Experience in building Agentic AI systems, including planning, reasoning, and decision‑making components.
• Required proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, transformers, LangChain, LangGraph, Autogen, LLamaIndex etc.).
• Required experience with Natural Language Processing (NLP) techniques, including text generation, understanding, and summarization.
• Proficiency in Python and common ML/NLP libraries (e.g., scikit‑learn, spaCy, Hugging Face Transformers).
• Hands‑on experience with anomaly detection techniques such as Isolation Forest, One‑Class SVM, Autoencoders, or statistical methods.
• Familiarity with NLP tasks such as classification, summarization, and named entity recognition.
• Experience with vectorization techniques (TF‑IDF, Word2Vec, BERT, etc.).
• Experience with vector databases (e.g., FAISS, Pinecone, ChromaDB).
• Exposure to LLMs and prompt engineering.

Good‑to‑Have

• Preferred experience with prompt engineering and fine‑tuning large language models.
• Preferred experience with knowledge graphs and semantic reasoning.
• Preferred experience with multi‑agent systems and their coordination.
• Preferred experience with explainable AI (XAI) techniques.
• Preferred experience with MLOps and model deployment pipelines.
• Experience with LangChain or Retrieval‑Augmented Generation (RAG) pipelines
• Familiarity with embedding strategies and chunking techniques
• Exposure to LLMOps tools and frameworks
• Understanding of Responsible AI principles and ethical AI development

Responsibilities / Expectations from the Role

1. Design and implement machine learning models for anomaly detection in time series and behavioral data.
2. Develop and maintain NLP pipelines for document processing and content generation.
3. Preprocess and clean structured and unstructured data using standard techniques.
4. Implement vectorization techniques and integrate with vector databases (e.g., FAISS, Pinecone, MongoDB Atlas Vector).
5. Work with embedding models (e.g., OpenAI, Hugging Face) to support semantic search and retrieval tasks.
6. Fine‑tune and evaluate LLMs for specific use cases such as summarization, classification, and test case generation.
7. Collaborate with backend engineers to expose ML models via APIs.
8. Monitor model performance using metrics like precision, recall, F1 score, and ROC‑AUC.
9. Contribute to proof‑of‑concept projects involving GenAI and RAG architectures.
10. Follow Responsible AI practices in model development and deployment.

Skills: langchain,generative ai,python,ai/ml